ArticleData in brief2021
The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma.
Article in Data in brief, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.The Lancet medical imaging & theranostics · 2026Article
- Amsterdam IMAging and Clinical GliOma Dataset; IMAGO.Scientific data · 2026Article
- Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.Journal of imaging · 2026Article
- A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation.Brain informatics · 2026Article
- The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.Scientific data · 2026Article
- Multimodal fusion of pathology and radiology foundation models for WHO 2021 glioma subtyping.NPJ precision oncology · 2026Article
- Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation.iScience · 2026Article
- Data-driven probabilistic mapping of the spatial and molecular landscape of glioma.Brain communications · 2026Article
- An integrated MRI-based diagnostic framework for glioma with incomplete imaging sequences and imperfect annotations.NPJ precision oncology · 2025Article
- Categorical and phenotypic image synthetic learning as an alternative to federated learning.Nature communications · 2025Article
- From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.Bioengineering (Basel, Switzerland) · 2025Article
- OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Mitigating Data Scarcity in the Classification of Glioma Molecular Subtypes: The Power of Generative Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Data Harmonization with StyleTransfer-GANs: Enhancing Non-Invasive IDH Classification in Brain Tumors.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Enhanced CATBraTS for Brain Tumour Semantic Segmentation.Journal of imaging · 2025Article
- Magnetic resonance imaging-based deep learning for predicting subtypes of glioma.Frontiers in neurology · 2025Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.NPJ precision oncology · 2024Article
- Two-Stage Training Framework Using Multicontrast MRI Radiomics forRadiology. Artificial intelligence · 2024Article
- Machine Learning-Based Prediction of GliomaCancers · 2024Article
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Authors and funding
12 authors.
Funding
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Abstract
The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumor segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19-86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods.
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